BinomialCommensuratePowerPrior class
Source:R/binomial_commensurate.R
BinomialCommensuratePowerPrior.RdThe commensurate power prior of GaussianCommensuratePowerPrior
for a binary endpoint, with the binomial likelihoods of both arms of each
study instead of a normal approximation of the risk difference; see the
comment at the top of R/binomial_commensurate.R for the model. The
posterior is computed on the lattice of BinomialLatticePrior.
Super classes
Model -> MCMCModel -> BinomialLatticePrior -> BinomialCommensuratePowerPrior
Public fields
methodMethod name.
heterogeneity_prior_familyFamily of the prior on the commensurability parameter.
borrows_power_parameterWhether the source likelihood is discounted by a power parameter.
n_tau_nodesQuadrature nodes on the commensurability parameter, before the adjustments of
commensurate_tau_quadrature().
Methods
Inherited methods
Model$calibrate_for_design()Model$check_data()Model$create()Model$empirical_bayes_update()Model$estimate_bayesian_operating_characteristics()Model$estimate_frequentist_operating_characteristics()Model$hypothesis_space_transformation()Model$inference_cache_scope()Model$plot_pdfs()Model$plot_posterior_pdf()Model$plot_prior_pdf()Model$posterior_beta_mixture()Model$posterior_mean()Model$posterior_moments()Model$posterior_quantile()Model$posterior_to_RBesT()Model$print_model_summary()Model$prior_to_RBesT()Model$simulation_for_given_treatment_effect()Model$test_decision()Model$vectorised_replicate_inference()MCMCModel$check_mcmc_config()MCMCModel$credible_interval()MCMCModel$draw_mcmc_prior()MCMCModel$inference()MCMCModel$posterior_cdf()MCMCModel$posterior_ess()MCMCModel$posterior_median()MCMCModel$posterior_pdf()MCMCModel$prepare_data()MCMCModel$prior_cdf()MCMCModel$prior_pdf()MCMCModel$sample_posterior()MCMCModel$sample_prior()MCMCModel$stan_sampler()MCMCModel$summary_rows()MCMCModel$uses_quadrature()BinomialLatticePrior$prior_elir_ess()BinomialLatticePrior$prior_given_control_rate()BinomialLatticePrior$quadrature_posterior()BinomialLatticePrior$quadrature_prior()BinomialLatticePrior$source_counts()
BinomialCommensuratePowerPrior$new()
Initialize the model.
Usage
BinomialCommensuratePowerPrior$new(prior, mcmc_config)BinomialCommensuratePowerPrior$kernels()
The prior kernels, computed once per worker and shared.
Returns
The output of binomial_commensurate_prior_kernels().
BinomialCommensuratePowerPrior$compute_posterior_parameters()
Record the posterior moments of the commensurability parameter and, for the commensurate power prior, of the power parameter.